ISBN: 9781098136581 - implementing mlops in the enterprise: a production-first approach (19 Ergebnisse)

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Paperback. Zustand: New. With demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine learning pipelines. This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production.Authors Yaron Haviv and Noah Gift take a production-first approach. Rather than beginning with the ML model, you'll learn how to design a continuous operational pipeline, while making sure that various components and practices can map into it. By automating as many components as possible, and making the process fast and repeatable, your pipeline can scale to match your organization's needs.You'll learn how to provide rapid business value while answering dynamic MLOps requirements. This book will help you:Learn the MLOps process, including its technological and business valueBuild and structure effective MLOps pipelinesEfficiently scale MLOps across your organizationExplore common MLOps use casesBuild MLOps pipelines for hybrid deployments, real-time predictions, and composite AILearn how to prepare for and adapt to the future of MLOpsEffectively use pre-trained models like HuggingFace and OpenAI to complement your MLOps strategy.…

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Paperback. Zustand: New. With demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine learning pipelines. This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production.Authors Yaron Haviv and Noah Gift take a production-first approach. Rather than beginning with the ML model, you'll learn how to design a continuous operational pipeline, while making sure that various components and practices can map into it. By automating as many components as possible, and making the process fast and repeatable, your pipeline can scale to match your organization's needs.You'll learn how to provide rapid business value while answering dynamic MLOps requirements. This book will help you:Learn the MLOps process, including its technological and business valueBuild and structure effective MLOps pipelinesEfficiently scale MLOps across your organizationExplore common MLOps use casesBuild MLOps pipelines for hybrid deployments, real-time predictions, and composite AILearn how to prepare for and adapt to the future of MLOpsEffectively use pre-trained models like HuggingFace and OpenAI to complement your MLOps strategy.…

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Paperback. Zustand: New. With demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine learning pipelines. This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production.Authors Yaron Haviv and Noah Gift take a production-first approach. Rather than beginning with the ML model, you'll learn how to design a continuous operational pipeline, while making sure that various components and practices can map into it. By automating as many components as possible, and making the process fast and repeatable, your pipeline can scale to match your organization's needs.You'll learn how to provide rapid business value while answering dynamic MLOps requirements. This book will help you:Learn the MLOps process, including its technological and business valueBuild and structure effective MLOps pipelinesEfficiently scale MLOps across your organizationExplore common MLOps use casesBuild MLOps pipelines for hybrid deployments, real-time predictions, and composite AILearn how to prepare for and adapt to the future of MLOpsEffectively use pre-trained models like HuggingFace and OpenAI to complement your MLOps strategy.…

Taschenbuch. Zustand: Neu. Neuware - With demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine learning pipelines. This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production. Authors Yaron Haviv and Noah Gift take a production-first approach. Rather than beginning with the ML model, you'll learn how to design a continuous operational pipeline, while making sure that various components and practices can map into it. By automating as many components as possible, and making the process fast and repeatable, your pipeline can scale to match your organization's needs. You'll learn how to provide rapid business value while answering dynamic MLOps requirements. This book will help you: - Learn the MLOps process, including its technological and business value - Build and structure effective MLOps pipelines - Efficiently scale MLOps across your organization - Explore common MLOps use cases - Build MLOps pipelines for hybrid deployments, real-time predictions, and composite AI - Build production applications with LLMs and Generative AI, while reducing risks, increasing the efficiency, and fine tuning models - Learn how to prepare for and adapt to the future of MLOps - Effectively use pre-trained models like HuggingFace and OpenAI to complement your MLOps strategy.…

Zustand: New. &Uumlber den AutorYaron Haviv is a serial entrepreneur who has been applying his deep technological experience in data, cloud, AI and networking to leading startups and enterprise companies since the late 1990s. As the co-founder and CT.

Paperback. Zustand: new. Paperback. With demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine learning pipelines. This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production. Authors Yaron Haviv and Noah Gift take a production-first approach. Rather than beginning with the ML model, you'll learn how to design a continuous operational pipeline, while making sure that various components and practices can map into it. By automating as many components as possible, and making the process fast and repeatable, your pipeline can scale to match your organization's needs. You'll learn how to provide rapid business value while answering dynamic MLOps requirements. This book will help you: Learn the MLOps process, including its technological and business value Build and structure effective MLOps pipelines Efficiently scale MLOps across your organization Explore common MLOps use cases Build MLOps pipelines for hybrid deployments, real-time predictions, and composite AI Learn how to prepare for and adapt to the future of MLOps Effectively use pre-trained models like HuggingFace and OpenAI to complement your MLOps strategy This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Zustand: New. With demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine learning pipelines. This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production.Authors Yaron Haviv and Noah Gift take a production-first approach. Rather than beginning with the ML model, you'll learn how to design a continuous operational pipeline, while making sure that various components and practices can map into it. By automating as many components as possible, and making the process fast and repeatable, your pipeline can scale to match your organization's needs.You'll learn how to provide rapid business value while answering dynamic MLOps requirements. This book will help you:Learn the MLOps process, including its technological and business valueBuild and structure effective MLOps pipelinesEfficiently scale MLOps across your organizationExplore common MLOps use casesBuild MLOps pipelines for hybrid deployments, real-time predictions, and composite AILearn how to prepare for and adapt to the future of MLOpsEffectively use pre-trained models like HuggingFace and OpenAI to complement your MLOps strategy.…

Taschenbuch. Zustand: Neu. Implementing MLOps in the Enterprise | A Production-First Approach | Yaron Haviv (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2024 | O'Reilly Media | EAN 9781098136581 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu. …